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-title: SigmaTriple
-emoji: 🔍
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: "1.32.0"
-app_file: app.py
-pinned: false
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# SigmaTriple: Knowledge Graph Extraction from Markdown
-
-This Hugging Face Space provides a Streamlit interface for extracting knowledge graphs from markdown text using the [SciPhi/Triplex](https://huggingface.co/sciphi/triplex) model.
-
-## Features
-
-- **Extract Knowledge Graphs**: Automatically identify entities and relationships from markdown text
-- **Customizable Entity Types and Predicates**: Define the types of entities and relationships you want to extract
-- **Batch Processing**: Process large markdown files efficiently using vllm
-- **Interactive Visualization**: View the extracted knowledge graph as an interactive network diagram
-- **File Upload Support**: Upload markdown files directly or input text manually
-
-## How It Works
-
-1. The application uses the SciPhi/Triplex model, which is fine-tuned for knowledge graph extraction
-2. Markdown text is processed to extract plain text content
-3. For large texts, batch processing is applied with overlapping chunks to ensure context is maintained
-4. The model identifies entities and relationships based on the specified entity types and predicates
-5. Results are parsed and visualized as an interactive knowledge graph
-
-## Usage
-
-1. **Configure Entity Types and Predicates**:
-   - In the sidebar, customize the entity types (e.g., PERSON, ORGANIZATION) and predicates (e.g., WORKS_AT, FOUNDED) you want to extract
-   
-2. **Input Text**:
-   - Choose between direct text input or file upload
-   - For text input, simply paste your markdown text in the provided area
-   - For file upload, select a markdown (.md), markdown (.markdown), or text (.txt) file
-
-3. **Extract Knowledge Graph**:
-   - Click the "Extract Knowledge Graph" button to process the text
-   - View the raw model output, extracted triplets table, and interactive visualization
-
-## Technical Details
-
-- Uses the SciPhi/Triplex model for knowledge graph extraction
-- Implements vllm for efficient batch processing when available
-- Falls back to standard transformers library if vllm is not available
-- Visualizes knowledge graphs using NetworkX and PyVis
-
-## Example Use Cases
-
-- **Research Papers**: Extract key concepts and relationships from academic papers
-- **Documentation**: Create knowledge graphs from technical documentation
-- **Content Analysis**: Identify key entities and relationships in articles or blog posts
-- **Educational Content**: Visualize relationships between concepts in educational materials
-
-## Limitations
-
-- The quality of extraction depends on the clarity and structure of the input text
-- Very large documents may require significant processing time
-- The model may not capture all relationships, especially those requiring deep contextual understanding
-
-## Credits
-
-- [SciPhi/Triplex Model](https://huggingface.co/sciphi/triplex)
-- [vllm](https://github.com/vllm-project/vllm) for efficient batch processing
-- [Streamlit](https://streamlit.io/) for the web interface
-- [NetworkX](https://networkx.org/) and [PyVis](https://pyvis.readthedocs.io/) for graph visualization